data analysis with spss : one-way anova
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Data Analysis with SPSSData Analysis with SPSS
One-way ANOVAOne-way ANOVA
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Analyze Compare Means One-Way Anova
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Example:
We want to examine whether there are significant differences in the monthly salary of employees from different age groups.
Dependent variable : Monthly SalaryIndependent variable : Age Group
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MONTHLY SALARY OF RESPONDENT
AGE GROUP OF RESPONDENT
Dependent List
Factor
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Press “Post Hoc” Multiple Comparisons Dialog Box
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In this example, I have chosen “Scheffe”. Then press “Continue”
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Press “OK” to execute
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Oneway
ANOVA
MONTHLY SALARY OF RESPONDENT
351.208 2 175.604 132.032 .000
889.778 669 1.330
1240.987 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
F = 132.032, Sig. = .000
Shows that the mean salary of the three age groups are significantly different
We do not know which group means are different, post hoc test will indicate this
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Post Hoc Tests
Multiple Comparisons
Dependent Variable: MONTHLY SALARY OF RESPONDENT
Scheffe
-1.081* .111 .000 -1.35 -.81
-2.003* .123 .000 -2.30 -1.70
1.081* .111 .000 .81 1.35
-.922* .105 .000 -1.18 -.66
2.003* .123 .000 1.70 2.30
.922* .105 .000 .66 1.18
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
The mean difference is significant at the .05 level.*.
Scheffe Multiple Comparisons test shows that all the three group means are significantly different from one another, sig. (or p) ≤ 0.001
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Lets look at two other examples
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ANOVA
impgdevt
.376 2 .188 .370 .691
339.527 669 .508
339.902 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
ANOVA to test whether there is/are significant difference(s) in the means of “importance of growth and development” between employees of different age groups
F = 0.370, p = 0.691
p >0.05, so there is no significant difference between the means of the three age groups for the importance of “growth and development”
Example 1
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Post Hoc Tests
Multiple Comparisons
Dependent Variable: impgdevt
Scheffe
-.01747 .06887 .968 -.1864 .1515
.03839 .07613 .881 -.1484 .2251
.01747 .06887 .968 -.1515 .1864
.05586 .06507 .692 -.1038 .2155
-.03839 .07613 .881 -.2251 .1484
-.05586 .06507 .692 -.2155 .1038
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
All the significant levels are more than 0.05, so there is no difference in the means of the groups
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Example 2
ANOVA
penvr
3.975 2 1.987 3.911 .020
339.927 669 .508
343.902 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
ANOVA to test whether there is/are significant difference(s) in the means of “importance of safe work environment (penvr)” between employees of different age groups
F = 3.911, p = 0.02
p = 0.02, (i.e. ≤ 0.05), so there is significant difference between the means
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Multiple Comparisons
Dependent Variable: penvr
Scheffe
.08662 .06891 .454 -.0824 .2557
.20965* .07617 .023 .0228 .3965
-.08662 .06891 .454 -.2557 .0824
.12303 .06511 .169 -.0367 .2828
-.20965* .07617 .023 -.3965 -.0228
-.12303 .06511 .169 -.2828 .0367
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
The mean difference is significant at the .05 level.*.
Post Hoc Tests
Scheffe test shows that there is significant difference between a pair of means: “25 YEARS AND BELOW” and “36 YEARS AND ABOVE”, p = 0.023 (≤0.05)
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Thank You